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About This Role
Job Title: Data Scientist, Quality and Research
Location: Remote
Organization: Cardiovascular Associates of America (CVAUSA)
Job Type: Contractor, fulltime
Travel: 1\-3 times per year (for team meetings)
Reports to: Executive Director of CVAUSA Patient Safety Organization
Cardiovascular disease is the single largest contributor to premature death and disability in the world. At CVAUSA, we are dedicated to delivering the highest quality cardiovascular care and to advancing scientific discovery through clinical research, including randomized trials and observational and health outcomes research. As we build our clinical programs and our national network of cardiovascular clinical trials, we need strong team members who are passionate about improving cardiovascular care quality through quality management and developing novel therapies that reduce the burden of cardiovascular disease!
Position Summary
The Data Scientist for Quality and Research will support Cardiovascular Associates of America’s enterprise\-wide quality improvement and clinical research initiatives. This fully remote role will be evenly split between quality improvement and clinical research, including outcomes and health services research.
This role is ideal for a Data Scientist with solid healthcare analytics experience who can manage analyses independently within defined projects, collaborate effectively with clinical and operational stakeholders, and translate data into meaningful insights that support high\-quality, evidence\-based cardiovascular care.
Role Allocation
50% Quality \& Performance Measurement
50% Clinical and Outcomes Research
Key Responsibilities
Quality Measurement \& Performance Analytics ( 50%)
Support enterprise cardiovascular quality initiatives, including:
MIPS/MVPs, Clinically Integrated Network, and payer\-specific performance reporting
Internal clinical quality and patient safety initiatives
Analyze provider\-, practice\-, and network\-level quality performance data.
Develop and maintain dashboards, scorecards, and routine performance reports.
Identify trends, variation, and opportunities for quality improvement.
Support audit readiness and documentation related to quality reporting and payer programs.
Collaborate with physician leaders and quality staff to support quality improvement initiatives.
Clinical Research, Outcomes \& Health Services Research ( 50%)
Support Cardiovascular Associates of America’s clinical research portfolio, including:
Registry\-based and real\-world evidence studies
Outcomes research and health services research
Investigator\-initiated and industry\-sponsored studies
Assist with cohort development, data extraction, and statistical analyses.
Support study feasibility assessments and analytic plans under senior guidance.
Support efforts to ensure adherence to IRB, regulatory, and data governance requirements.
Contribute to abstracts, manuscripts, posters, and internal research reports.
Support dissemination of research findings to inform clinical practice and strategy.
Apply standardized analytic workflows and best practices.
Assist with development and maintenance of analytic documentation and reporting templates.
Collaboration \& Communication
Collaborate with physician leaders, practice administrators, and quality and research committees.
Present analytic findings in a clear, concise manner to clinical and operational audiences.
Participate in enterprise quality and research meetings as needed.
Qualifications
Required
Bachelor’s degree in public health, epidemiology, biostatistics, health services research, data science, healthcare administration, or a related field.
5\+ years of experience in clinical or outcomes research, including quality improvement analytics.
Deep experience working with clinical datasets (EHR, registry, and/or claims data).
Fluent in SQL, SAS, and Microsoft Excel.
Deep experience with PowerBI, including building and maintaining dashboards.
Familiarity with healthcare quality programs or clinical research workflows.
Strong written and verbal communication skills.
Preferred
Master’s degree (MPH, MS, MHA, or equivalent).
Proficiency with Python and R
Experience in cardiovascular medicine or cardiology practice environments.
Exposure to IRB\-regulated research or observational studies.
Familiarity with CMS quality programs, MIPS/MVPs, ACOs, or payer quality reporting.
Key Attributes
Comfortable working independently within defined project scopes and self\-motivated.
Excels in fast\-paced, “startup\-like” environment.
Detail\-oriented with strong analytical and organizational skills.
Able to balance multiple priorities in a fully remote work environment.
Extremely well organized
Likes to work collaboratively, and responsive to feedback.
Motivated by improving quality and outcomes in cardiovascular care.
Why Join Cardiovascular Associates of America
At CVAUSA, you’ll help improve and measure quality and advance scientific discovery at a national scale, and as part of the nation’s largest network of cardiovascular care professionals.
You’ll have an opportunity to work closely with and learn from physician, quality, and research leaders. Your work will be more than just numbers and data; it will be foundational for our efforts to improve patient care and save lives. You’ll join a mission\-focused organization dedicated to excellence in cardiovascular care and clinical research.
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Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Cardiovascular Associates of America, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Cardiovascular Associates of America AI Hiring
Cardiovascular Associates of America has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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